@article{iorio2010discovery,
  title={Discovery of drug mode of action and drug repositioning from transcriptional responses},
  author={Iorio, F. and Bosotti, R. and Scacheri, E. and Belcastro, V. and Mithbaokar, P. and Ferriero, R. and Murino, L. and Tagliaferri, R. and Brunetti-Pierri, N. and Isacchi, A. and others},
  journal={Proceedings of the National Academy of Sciences},
  volume={107},
  number={33},
  pages={14621},
  year={2010},
 abstract={ To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules,
together with pattern-matching software to mine these data. We demonstrate that this
"Connectivity Map" resource can be used to find connections among small molecules sharing a
mechanism of action, chemicals and physiological processes, and diseases and drugs. These results
indicate the feasibility of the approach and suggest the value of a large-scale community
Connectivity Map project.}
}

@article{lamb2006connectivity,
  title={The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease},
  author={Lamb, J. and Crawford, E.D. and Peck, D. and Modell, J.W. and Blat, I.C. and Wrobel, M.J. and Lerner, J. and Brunet, J.P. and Subramanian, A. and Ross, K.N. and others},
  journal={science},
  volume={313},
  number={5795},
  pages={1929},
  year={2006},
  abstract={A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a “drug network” of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compound-targeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be “repositioned” as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it). }
}

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}

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@article{iorio2009identifying,
  title={Identifying network of drug mode of action by gene expression profiling},
  author={Iorio, F. and Tagliaferri, R. and Bernardo, D.},
  journal={Journal of Computational Biology},
  volume={16},
  number={2},
  pages={241--251},
  year={2009},
  publisher={Mary Ann Liebert, Inc. 2 Madison Avenue Larchmont, NY 10538 USA}
}

@article{subramanian2005gene,
  title={Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles},
  author={Subramanian, A. and Tamayo, P. and Mootha, V.K. and Mukherjee, S. and Ebert, B.L. and Gillette, M.A. and Paulovich, A. and Pomeroy, S.L. and Golub, T.R. and Lander, E.S. and others},
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}

@article{hu2009human,
  title={Human disease-drug network based on genomic expression profiles},
  author={Hu, G. and Agarwal, P.},
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  volume={4},
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}

@article{north2002note,
  title={A note on the calculation of empirical P values from Monte Carlo procedures},
  author={North, BV and Curtis, D. and Sham, PC},
  journal={American journal of human genetics},
  volume={71},
  number={2},
  pages={439},
  year={2002},
  publisher={Elsevier}
}




